Penetration Recognition in GTAW Welding Based on Time and Spectrum Images of Arc Sound Using Deep Learning Method

Abstract

The weld penetration rate is an important evaluation criterion for welding quality. However, it is difficult to identify the weld penetration state during GTAW welding process. This paper presents a new penetration recognition method based on time and spectrum images of arc sound using deep learning for DC GTAW welding. The time domain and spectrum images of the three penetration states from the non-periodic arc sound were used as the dataset for the penetration prediction model. VGG16, AlexNet, and custom convolutional neural network (CNN) were used to extract image features, and softmax was used to classify images for penetration recognition. The influence of image feature extraction networks, input methods, and different sampling methods on the recognition accuracy was deeply analyzed. The results show that the overall validation accuracy of the proposed model is approximately 96.2%. Particularly, the validation accuracy of the model in the excessive penetration state is approximately 100%. This study provides a new and feasible method for the online detection of weld penetration during the GTAW welding process.